SEKE 2012 Proceedings - Knowledge Systems Institute

A mobile application for stock market prediction using sentiment analysis
Kushal Jangid ∗
San Jose State University
Computer Engineering Department
San Jose, CA, USA
Magdalini Eirinaki
San Jose State University
Computer Engineering Department
San Jose, CA, USA
Pratik Paul †
San Jose State University
Computer Engineering Department
San Jose, CA, USA
Abstract
Lately, stock markets have been going through a lot of
volatility. Traditional methods of stock market prediction,
which involved using historical stock prices to predict future
price have shown to be insufficient under certain circumstances.
In this paper we present a mobile application
that employs a different approach to predicting the stock
price, namely the news related to that company. The prediction
algorithm determines whether the overall sentiment
related to the company, as expressed by the news stories, is
good or bad and assigns a sentiment score. The system then
uses machine learning to predict the percentage fluctuation
of the company’s stock based on this score. The algorithm
is integrated in a mobile application that helps users try out
various market strategies based on our prediction engine.
This is achieved by performing simulated trades using virtual
money. It also provides real-time stock quotes, and the
latest financial news. In this paper we present the overall
system architecture and design of this application, as well
as details of the prediction process.
1 Introduction
There is a tremendous research going on in the field of
stock market prediction. There are a number of artificial intelligence
and machine learning techniques that have been
used for analyzing price patterns and predicting stock prices
and index. Among the most commonly used are Neural
Networks, which have the ability to learn non-linear relationships
based on trading information. This allows mod-
∗ Author’s current affiliation is E*Trade Financial Corporation
† Author’s current affiliation is Reply.com
eling of non-linear dynamic systems such as stock markets
more precisely. Support Vector Machines (SVM) seem to
be the next most popular approach to stock market prediction.
It has been successful in classification task and
regression tasks, especially on time series prediction and
financial-related applications.
There has also been some research on hybrid algorithms
which combine different algorithms toimprove the prediction
accuracy. One example is the combination of Genetic
Algorithm and Support Vector Machine. The experiments
conducted indicate that the accuracy achieved by combining
the algorithms is better than the results from algorithms
individually [3]. The authors [5] claim that Support Vector
Machine when combined with Boosting provides better
accuracy.
A more recent trend, however, is to depart from using
pure machine learning on the numbers, and instead to also
focus on other input such as the financial news to determine
the stock fluctuation. Most of the people are familiar that
company’s earnings report and good/bad news associated
with the company tend to affect its price. Looking at its importance,
people have started combining text mining with a
couple of different algorithms in order to predict the stock
price movement. One example is where some researchers
combined text mining approach with time series algorithm
to map the fluctuation in the stock price [10]. There are
other examples where the text mining algorithm is combined
with SVM [7].
Most of the applications for stock market prediction
are web-based, and there are not many available for smart
phones. However, as smart phones become an integral part
of everyday lives and people are using them as portable devices,
the need to trade on the go is evident. After interviewing
a couple of people working on Wall Street, we found
out that having a prediction system available on your phone
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